---
title: "Plotly: Shiny-like selectors without shiny"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: spacelab
    source_code: embed
    fontsize: 16pt
---

World Bank Data: WDI
=======================

We're using data from **The World Bank**: *World Development Indicators*, WDI.

The data is accessed via the **WDI R package**. To make sure we get the newest list of available data series, we update the old list stored in the package with the current one from the website using the *WDIcache()* function.

```{r setup}

library(knitr)
library(kableExtra)
library(flexdashboard)
library(WDI)
library(tidyverse)
library(plotly)
library(crosstalk)
library(ggthemes)

knitr::opts_chunk$set(echo = FALSE)

cache <- WDIcache()

# Noteworthy indicators, but with many missings:
# renewable_energy_consumption = "EG.FEC.RNEW.ZS",
# GDP_per_cap = "6.0.GDPpc_constant",
# GDP_usd = "6.0.GDP_usd",
# GNI_per_cap = "6.0.GNIpc"
# obesity_pct = "HF.STA.OB18.ZS",
# overweight_pct = "HF.STA.OW18.ZS"

data_org <- WDI(country = "all",
            indicator = c(survival_15_60 = "HD.HCI.AMRT",
                          life_expectancy = "SP.DYN.LE00.IN",
                          air_pollution = "EN.ATM.PM25.MC.M3"),
            extra = TRUE,
            cache = cache)
 
saveRDS(data_org, "WDI_data.rds")

# data_org <- readRDS("WDI_data.rds")

data <- data_org %>% 
  filter(region != "Aggregates") %>% 
  filter(!is.na(air_pollution) & !is.na(life_expectancy)) %>% 
  filter(year == 2017)

data %>% 
  select(country, year, air_pollution, life_expectancy, region, capital) %>% 
  head() %>% 
  kbl() %>% 
  kable_paper("hover", full_width = FALSE,
              lightable_options = "striped",
              font_size = 18)

```

The indicators we're interested in are **air pollution**, defined as *mean annual exposure (micrograms per cubic meter)*, and **life expectancy** at birth in years. 


ggplot2 and ggplotly
====================

A quick start can be made using a static ggplot2 diagram. Until quite recently, I only used plotly via the *ggplotly()* function to turn a static plot into an interactive one with mouse-over-effects, like this:

Column
------

```{r static-plot, fig.width = 9, fig.height = 6}

p <- data %>% 
  ggplot(aes(x = air_pollution, y = life_expectancy, group = country, color = region)) +
  geom_point(size = 3, alpha = 0.8) +
  theme_economist_white(base_family = "Verdana") +
  # scale_color_economist() +
  # scale_colour_viridis_d() +
  scale_color_brewer(palette = "Dark2") +
  labs(title = "Life Expectancy vs. Air Pollution 2017",
       x = "Air Pollution
       Mean Annual Exposure in micrograms per cubic meter",
       y = "Life Expectancy\nat birth in years",
       caption = "Data Source: World Bank, World Development Indicators (WDI),
                  obtained via the WDI R package, version 2.7.1") +
  theme(legend.position = "right",
        legend.text = element_text(size = 14),
        legend.title = element_blank())

p

```


Column
------

```{r ggplotly, fig.width = 9, fig.height = 6}

ggplotly(p)

# ggplotly(p) %>% 
#   layout(annotations = list(
#     x = 2016, y = 250, xanchor = 'right', 
#     showarrow = FALSE, 
#     # xshift = 0, yshift = 0, xref = 'paper', yref = 'paper',
#     text = "Data Source: World Bank, World Development Indicators (WDI),
#                          obtained via the WDI R package, version 2.7.1"),
#     font = list(size = 11)
#     )

```

We could use facetting to get a better overview of regions.

However, today we prefer an interactive approach.

plotly and crosstalk: Select Regions interactively
==================================================

```{r plotly-crosstalk}

shared_data <- data %>% 
  select(country, air_pollution, life_expectancy, region) %>% 
  na.omit() %>% 
  mutate(region = stringr::str_replace(region, "&", "and"),
          air_pollution = round(air_pollution, 1),
          life_expectancy = round(life_expectancy, 1)) %>% 
  SharedData$new()

p <- shared_data %>% 
  plot_ly(x = ~air_pollution, y = ~life_expectancy, color = ~region,
              hoverinfo = "text",
              text = ~paste("Country:", country,
                            "<br>Region:", region,
                            "<br>Air Pollution:", air_pollution,
                            "<br>Life Expectancy:", life_expectancy)) %>% 
  group_by(region) %>% 
  add_markers(size = 3) %>%
  layout(xaxis = list(title = "Air Pollution<br>Mean Annual Exposure in micrograms per cubic meter"),
         yaxis = list(title = "Life Expectancy<br>at birth in years"),
         legend = list(font = list(size = 16)))

# Combining several selectors

bscols(widths = c(3, 9),
       list(
            filter_checkbox(id = "region", label = "Region",
                    sharedData = shared_data, group = ~region),
            filter_select(id = "country", label = "Country",
                    sharedData = shared_data, group = ~country),
            filter_slider(id = "slider_ap", label = "Air Pollution",
                    sharedData = shared_data, column = ~air_pollution),
            filter_slider(id = "slider_le", label = "Life Expectancy",
                    sharedData = shared_data, column = ~life_expectancy)
      ),
       p)

```

Notes
=====

The **crosstalk** package is a great companion to **plotly**, enabling us to make interactive selections without using shiny. Both plotly and crosstalk were created by **Carson Sievert**. See *help(package = "plotly")* / *help(package = "crosstalk")* to see further contributors.

To set up this functionality, we need to set up a **Shared Data Object**. This is based on the R6 system for object oriented programming in R.

Note that we apply the specific plotly syntax, which requires the tilde ~ to specify variables, and uses the pipe %>% rather than the + operator like ggplot2.